Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients.

Performance of deep learning models for predicting persistent CSC. The  For the test set, the accuracy percentages were 80.72%, 92.00%, 64.80%, 92.00%, and 63.20% for B-scan, retinal thickness, mid-retinal, EZ, and choroid, respectively. The F1 scores by set were 0.6800, 0.8718, 0.3125, 0.8718, and 0.1154, respectively. The accuracy, precision, recall, specificity, F1 score, and kappa values for each model are summarized in Table 2.
Upon concatenation of the two highest performing image sets, the accuracy percentages for the validation set were 96.80%, 86.40%, and 96.00%, for B-scan + EZ, B-scan + retinal thickness, and EZ + retinal thickness, respectively. For the test set, the accuracy was 95.20%, 88.00%, and 92.80% for each set, respectively. The accuracy, precision, recall, specificity, F1 score, and kappa values for each model trained using concatenated images are summarized in Table 3.
Heatmap analysis with Grad-CAM highlighted areas of shallow irregular pigment epithelial detachment (PED) and areas of increased reflectivity beneath the detached retina as important area (Fig. 1) on OCT B-scan. Also, areas of the neurosensory retinal detachment with or without outer retinal defects are highlighted on OCT en face images of EZ layer (Fig. 2). Optical coherence tomography angiography was available in some cases of persistent CSC. In these cases, areas of PED containing RPE undulation (retinal pigment epithelium) on B-scan was revealed to be an important area for the decision but did not conceal choroidal neovascularization (Fig. 3).

Discussion
In this study, we developed DL models to predict the prognosis of CSC patients and whether the disease will be self-resolving or persistent for the next 6 months. The results of our study showed that DL model training using pretrained ResNet50 can predict the prognosis of CSC eyes by analyzing OCT B-scans and en face images of retinal thickness, EZ layer, and choroid layer with good performance. In addition, the performance of DL models was better when these image sets were concatenated. Diagnosis, classification, and prognosis prediction of a disease using DL has become an active field of imaging research in ophthalmology, especially the retina 6,[8][9][10][11][12][13] . In CSC, diagnosis and classification of the disease, detection of subretinal fluid, and prediction of post-therapeutic visual acuity have been investigated using DL 9,10,12-15 . Most previous studies have used fundus photography or OCT B-scans alone for development of DL models. The value of en face images in CSC has been emphasized by numerous studies 12,[16][17][18] . The pathologic features of the RPE and choroid in CSC eyes are well visualized on en face images 19 . Therefore, this study utilized multi-layer OCT en face images along with B-scan images for automated prediction of the prognosis of CSC.
In predicting the prognosis of CSC patients, OCT image sets of B-scans, retinal thickness, and the EZ layer showed good performance in this study. Xu et al. applied DL to predict visual acuity in CSC patients using clinical features, collected features from OCT B-scan images, and showed that the DL model could predict the visual outcome of CSC after treatment with low mean absolute error 13 . This study confirmed that OCT B-scan images can be used to predict the outcome of CSC using a DL model. Nonetheless, the recall and F1 scores produced by the DL model using B-scans alone were unsatisfactory.
En face image sets of the EZ and retinal thickness showed better accuracy, sensitivity, and specificity in DL prediction models of CSC prognosis. The en face EZ layer reflects changes in the outer or inner photoreceptor segment as well as the EZ (IS/OS junction). Significant loss in photoreceptors and an elongated photoreceptor outer segment length are characteristic findings and are also associated with visual prognosis in patients with chronic CSC 4,20,21 . The good performance of the DL model using the EZ layer en face images suggests that changes to the adjacent EZ layers play an important role in the prognosis of CSC.
The integrity of EZ observed on OCT B-scan and CMT was shown to be important in predicting visual acuity at 1, 3, and 6 months 13 . OCT volume scans were used to estimate post-treatment retinal function in CSC patients in a study by Pfau et al. 15 . Their study showed that localized retinal sensitivity after treatment can be inferred from the thicknesses of the retinal layers in CSC eyes using machine learning (random forest after feature extraction). Similarly, in this study, the thickness map of the entire retinal layer, which contains information on the thicknesses of retinal layers and CMT, could predict the prognosis of CSC using DL. It is not clear whether higher CMT or shallow diffuse CMT was prognostic factor for persistent CSC in this study. Arora et al. showed that shallow subretinal fluid was associated with recurrent or complicated CSC 22 . This might be the point for predictionin this model, but this interpretation requires caution since the association between CMT and visual outcome depends on types of CSC. An en face image of the mid-retinal layer did not show good results for training the DL model.  12 . Nonetheless, en face images of the choroid were not useful for a DL model to predict the outcome of CSC. Although a previous report implied different characteristics of the choroidal vasculature between acute and chronic CSC, we suggest that these features are not significant enough for use in a DL prediction model 16 .
A Grad-CAM heatmap analysis of OCT B-scan images showed that the hyperreflective area beneath the detached retina was important for determining the prognosis of the eye as persistent. The hyperreflective area indicates an elongated photoreceptor outer segment, which is well recognized as an important feature of chronic CSC 25 . In addition, shallow irregular PED areas were highlighted on the heatmaps. A small, flat, irregular PED could be a marker of chronic or recurrent CSC and implies genetic overlap of patients with AMD, which can explain the refractoriness of the condition 26,27 .
Important limitations of this study include the limited performance according to the OCT device as this study only included images obtained with one HD-OCT system. Currently, high performance of a DL model requires input data from the same OCT system. Additionally, although we used a recent consensus for CSC classification that was developed using multimodal imaging 28 , there might be some controversy in defining CSC as acute or persistent in the CSC eyes that did not resolve during months three to five. However, we believe that there will be no controversy in that the disease is considered refractory if it persists for more than 6 months; predicting this entity at baseline can be useful for both doctors and patients. Further research involving multimodal images from many OCT machines and more retinal specialists are warranted to validate the results of the current investigation.
This study was unique in that it assessed the feasibility of a DL model using multiple OCT imaging sets for predicting the prognosis of CSC, which few previous studies have done. OCT volume scan contains a full set of B-scan images and en face image can be obtained from the volume scan. Using the full set of B-scam images may provide more information on the status of retina and choroid. However, the volume scan contains up to hundreds of B-scan images, which requires tremendous resources and time for training and validating using CNN. The DL models developed in this study utilized OCT slabs that were automatically provided by the commercial OCT viewer program and can easily be applied in practice. And en face images used in the study contains information www.nature.com/scientificreports/ which had been proved to be valuable in CSC. Further progression in computational limit may enable using whole set of images and improve the performance of DL models.
In conclusion, this study assessed the performance of DL models for CSC prognosis using multiple OCT image sets. The results revealed good performance of DL models created using OCT B-scans, retinal thickness, and EZ en face images. Concatenation of these image sets increased the performance of a DL model. Heatmaps revealed that a shallow irregular PED and an elongated photoreceptor outer segment on B-scans and a defective en face EZ area were important areas for prognosis. This automated prediction system will aid in individualized medical care for patients with CSC.

Methods
This study was approved by the Institutional Review Board of Bucheon St. Mary's Hospital, which waived the need for written informed consent because of the study's retrospective design. The study was conducted in accordance with the tenets of the Declaration of Helsinki.
Participants. The inclusion criteria were consecutive patients 18 years or older who visited Bucheon St.
Mary's or St. Vincent Hospital between April 2016 and January 2022 and were diagnosed with CSC. Diagnosis was determined using high-definition OCT (Cirrus 4000 or Cirrus 6000; Carl Zeiss Meditec, Jena, Germany) and fluorescein angiography. OCT angiography image was assessed for presence of neovascularization in available patients. In patients with bilateral disease, the eye with the most severe symptoms was chosen for the study. Clinical information of age, sex, laterality of the diseased eye, baseline best-corrected visual acuity (BCVA), and BCVA at 6 months was collected from a chart review.

Image preparation, model training, and performance metrics. All patients underwent HD-OCT
Statistics. Statistical analysis was performed using MATLAB 2021b. T-tests were used to compare clinical features between groups, and the chi-square test was used to compare categorical variables. Accuracy, precision, recall, specificity, and F1 scores were calculated for each model. The kappa score mean and standard deviation (SD) were used to determine the agreement between truth and each model. Continuous variables are presented as mean ± SD.
Ethics approval and consent to participate/publication. This study was conducted in accordance with the tenets of the Declaration of Helsinki. The study was approved by the Institutional Review Board of Bucheon St. Mary's Hospital, which waived the need for written informed consent because of the study's retrospective design (HC20TISI0091).